The dynamism of Bayesian non-parametric methods in making data-driven decisions based on infinite models, has motivated many studies in various fields. This talk presents the application of HDP switching models in action recognition for video data. We have proposed a stream-based online model, capable of adaptive learning of unlimited actions with real-time performance. Results on three action datasets prove a remarkable performance.
This study is further followed by integrating other BNP models like IBP to enhance pattern recognition in high-dimensional contexts.